标题： 绝对正则过程的广义可加条件分位数估计 显示英文标题

标题： Estimating Generalized Additive Conditional Quantiles for Absolutely Regular Processes

摘要： 我们提出了一种非参数方法来估计条件分位数函数，该方法允许广义可加模型的规范，并具有未知的连接函数。 该模型包含单指标、可加和乘法分位数回归模型。 基于完整的局部线性多项式展开，我们首先得到每个可加成分的所提出分位数估计量的渐近表示。 然后，通过注意到它对应于给定所有可加成分之和的响应变量的条件分位数函数，来估计连接函数。 假设观测值是从严格平稳且绝对正则的过程中抽取的样本。 我们提供了每个所提出估计量的（一致）一致性速率、二阶渐近展开和点态渐近正态性的结果。 显示英文摘要

摘要： We propose a nonparametric method for estimating the conditional quantile function that admits a generalized additive specification with an unknown link function. This model nests single-index, additive, and multiplicative quantile regression models. Based on a full local linear polynomial expansion, we first obtain the asymptotic representation for the proposed quantile estimator for each additive component. Then, the link function is estimated by noting that it corresponds to the conditional quantile function of a response variable given the sum of all additive components. The observations are supposed to be a sample from a strictly stationary and absolutely regular process. We provide results on (uniform) consistency rates, second order asymptotic expansions and point wise asymptotic normality of each proposed estimator.